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5.2 Optimizer Features and Oddities

When testing queries, always remember to use realistic data. A common source of problems with MySQL is the query optimizer's handling of test data. It often does surprising things. If you don't know what it's doing and why (and it rarely tells you why), you may spend a lot of time tracking down a problem that really isn't there. Or, worse yet, you may embarrass yourself asking about it on the MySQL mailing list, only to learn that you've created the problem all on your own.

In general, MySQL uses an index when it is reasonably confident that doing so is more efficient than not doing so. This leads to false negatives during testing. The false negative tends to occur in the two situations that we'll now investigate.

5.2.1 Too Little Diversity

Even if you have a lot of data (thousands of rows or more), MySQL may choose to ignore your indexes some of the time if your data doesn't have sufficient diversity. Why might that happen? Imagine you have a table that contains historical climate data for most world cities:



  city       VARCHAR(100) NOT NULL,

  high_temp  TINYINT      NOT NULL,

  low_temp   TINYINT      NOT NULL,

  the_date   DATE         NOT NULL,

  INDEX (city),

  INDEX (the_date),


Rather than loading all two million records, you load two years worth of data (1980 and 1981) to test. After some testing, you find that queries that need to access many of the records are using full table scans rather than the the_date index. For example, to find the average high temperature in 1980, you might write something like this:

SELECT AVG(high_temp) FROM weather

WHERE the_date BETWEEN '1980-01-01' AND '1980-12-31';

Having data from only 1980 and 1981 loaded, that query needs to examine 50% of the rows in the weather table. In such a case, MySQL decides that it is faster to simply scan the entire table.

How does it know? When you cross a certain threshold, it is slower to locate rows using an index than to read them sequentially. For MySQL, the cutoff point is roughly 30%. The number is chosen by the MySQL developers based on their extensive experience (and knowledge of the code) and is subject to change from release to release. The actual number is specific to each storage engine: InnoDB has a different threshold than MyISAM tables, and so forth.

The main reason index performance is worse in these circumstances goes all the way down to the hardware: disk seek performance. Indexes are always sorted, but the data on disk is not. Using an index means accessing the rows in index-sorted order rather than in the order they reside on disk. The end result is more time spent moving around the disk and less time reading data. Sequential reads are always going to be faster than random seeks. If you're lucky enough to be using a RAM disk, most of the overhead vanishes.

You can draw two conclusions from this knowledge. First, if a table really is going to remain very small, you may want to leave off the indexes. (Unique indexes are an exception to this rule. Without them you can't enforce a unique constraint on the table.) The second conclusion merely reinforces what we said earlier—always use a representative data set for your testing. It should be representative both in terms of size and diversity.

One special case that must be mentioned is that of index-only queries. If you happen to write a query that requires only columns contained within a single index, you'll be pleasantly surprised. MySQL is smart enough to realize that all the required data is present in the index, so it doesn't bother to fetch any of the rows from disk. This, obviously, provides you with excellent performance.

5.2.2 Index-Based Ordering

One of MySQL's weak points is sorting. It can usually fetch 15,000 rows in a heartbeat, but if you happen to need them in any particular order it may take quite a bit more time.[2]

[2] Of course, performance is always relative. we've seen queries that MySQL answered in 20 ms take 200 ms after adding an ORDER BY clause. For many applications, 200 ms is still quite fast.

The problem is really two-fold. First, sorting is simply more work, and work takes time. Aside from adding a faster CPU, there's no avoiding that fact. If you're not sorting on a computed field, your first instinct is likely to add an index on the sorting column. Unfortunately, that rarely helps. As you'll remember from Chapter 4, MySQL uses at most one index per table per query. Odds are that you're already using an index on the table in question, so MySQL will not touch your new index.

The solution to the second problem also goes back to Chapter 4. Add the sorting column as a second part in the existing index. By doing so you get the best of both worlds. You'll have an index MySQL can use to quickly locate rows (just as before) and an index that provides order to the data. That removes the need for MySQL to make a sorting pass over the results.

Going back to the weather example, to speed up queries like this:

SELECT * FROM weather WHERE city = 'Toledo' ORDER BY the_date DESC

you'd change the index on city to an index on (city, the_date):

ALTER TABLE weather DROP INDEX city, ADD INDEX (city, the_date)

Remember that the order of columns is significant. The leftmost prefix rule dictates that city must appear first in the index to be used for that query.

Taking things a step further, you might then be tempted to remove the single index on the_date. Don't do it unless you're sure there are no queries using the_date in their WHERE clause. A query based on the_date can't be satisfied using the new index on (city, the_date) because the_date isn't a leftmost prefix in the index.

5.2.3 Impossible Queries

MySQL performs a basic logical analysis of the WHERE clause of every query. In doing so, it can often detect when you've asked for something that doesn't make any sense:

SELECT * FROM mytable WHERE id < 5000 and id > 30000

If it finds an impossible WHERE clause, it returns zero records, sparing the expense of running an otherwise pointless and possibly expensive query.

If you suspect that MySQL has optimized away an impossible WHERE clause, simply ask it to EXPLAIN the query. If you see a result like this:

mysql> SELECT * FROM mytable WHERE id < 5000 and id > 30000


| Comment                                             |


| Impossible WHERE noticed after reading const tables |


1 row in set (0.00 sec)

you'll know what it was thinking.

Aside from making a simple typo, it's unlikely that you'll run many queries like that. However, if you're building an application on top of MySQL and happen to make a typo or a serious logic error in the code, you can end up running lots of pointless queries before tracking down the problem. It's good to know that MySQL doesn't waste much time dealing with your illogical queries.

5.2.4 Full-Text Instead of LIKE

From Chapter 4, it's clear that full-text indexes are much faster than using a LIKE clause in your queries to search for a word or phrase. In the vast majority of cases, you should use a full-text index to tackle these types of problems.

However, there are times when this can be problematic. The query optimizer doesn't look very closely at full-text indexes when deciding which index to use for a table. In fact, if there's a usable full-text index, the optimizer will always prefer it regardless of how many rows it actually eliminates from the result set. Hopefully this will be fixed in a future version of MySQL.

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